Microblogs are increasingly gaining attention as an important information source in emergency management as a lot of valuable situational information is shared, both by citizens and official sources. However, current analyses focus on information shared during large scale incidents, with high amount of publicly available information. In contrast, in this talk we present the results of several studies on every day small scale incidents. The comparably low amount of information shared per event makes this significantly harder.
First, we show the results of a machine learning experiment for automatically detecting relevant information related to small scale incidents. With a precision of 82.2%, we are able to detect three different types of small scale incidents in microblogs. Second, we highlight the value of information present in microblogs for increasing situational awareness. For that, we demonstrate that incidents detected based on microblogs are correlated with real-world incidents. We also show the results of our analysis on user behavior during this type of incidents.